In the present study, we standardized an in vitro oviduct explants model for cattle and assessed the oviduct explants binding ability and phenotypic characteristics of spermatozoa obtained from breeding bulls with high- and low-sperm DNA fragmentation index (DFI%). https://www.selleckchem.com/products/ap-3-a4-enoblock.html Cryopreserved spermatozoa from Holstein Friesian crossbred breeding bulls (n = 45) with known field fertility were assessed for DFI% and were classified into either high DFI% or low DFI% category. Flow cytometry was used to assess sperm membrane integrity, acrosome reaction status, mitochondrial membrane potential and intracellular calcium concentrations. It was found that spermatozoa from bulls with low DFI% had significantly higher (P 2 times) in the bulls with low-DFI% as compared to high DFI% bulls. The correlation between binding index and DFI% was negative and significant (r = -0.528; P less then 0.05). Further, the binding index was positively correlated with conception rate (r = 0.703), intact sperm membrane (r = 0.631) and mitochondrial membrane potential (r = 0.609). It is inferred that sperm phenotypic characteristics and oviduct binding ability are impaired in breeding bulls with high sperm DFI%, which might be associated with low conception rates in these bulls.In this paper, we report a novel multiple amplification strategy for ultrasensitive near-infrared electrochemiluminescence (ECL) immunoassay in K2S2O8 solution. The realization of this strategy is based on the antenna effect of Eu-MOF (EuBTC) and a high efficiency catalysis of CoS2 hollow triple shelled nanoboxes (TSNBs). The H3BTC ligand in the antenna effect first undergoes π-π* absorption and a singlet-singlet electronic transition. Its energy passes through the intersystem to the triplet state, next transfers from the lowest excited triplet state to the vibrational energy level of the rare earth ion, finally realizing sensitizing center ion luminescence. Moreover, ionic reaction and structural advantages endow CoS2 TSNBs a dual signal enhancement effect. This sandwich-type ECL biosensor has a near-infrared luminescence in 800-900 nm, thus avoiding damage to the sample in the meantime. In practical diagnosis, the normal critical value of procalcitonin (PCT) ( less then 0.5 ng/mL) is much higher than the detection limit (3.65 fg/mL) and is in the detection range (10 fg/mL-100 ng/mL), which means that the ECL biosensor has a high sensitivity in the detection of PCT and meet the requirement for diagnosis of disease completely. Therefore, the strategy provides a feasible method for efficient and stable analysis of systemic inflammatory response such as fearful bacterial infection, hepatitis B, and peritonitis.This study first reported enzyme-free impedimetric biosensor-based molecularly imprinted polymers for selective and sensitive determination of L-hydroxyproline (L-hyp), a biomarker for the early diagnosis of bone diseases. In recent study, utilizing a single 3-aminophenylboronic acid (3-APBA) to create imprinted surfaces could result in a strong interaction and difficulty in removal of a template molecule. Hence, a mixture of monomer solution containing 3-APBA and o-phenylenediamine (OPD) in the presence of the L-hyp molecule was co-electropolymerized onto the screen-printed electrode using cyclic voltammetry (CV) to eradicate this mentioned limitation. The detection principle of this sensor is relied on alteration of mediator's charge transfer resistance (Rct) that could be obstructed by L-hyp occupied in imprinted surface. The successfully fabricated biosensor was explored by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and confocal scanning microscopy. Furthermore, the effect of polymer composition on the Rct response was systematically investigated. The result exhibited that the mixture of monomers could provide the highest change of Rct due to high selectivity from esterification of 3-APBA and from hydrogen bond of OPD surrounding the template. The sensor showed a significant increase in Rct in the presence of L-hyp, whereas no observable resistance change was detected in the absence thereof. The calibration curve was obtained in the range from 0.4 to 25 μg mL-1 with limits of detection (3SDblank/Slope) and quantification (10SDblank/Slope) of 0.13 and 0.42 μg mL-1, respectively. This biosensor exhibited high selectivity and sensitivity and was successfully applied to determine L-hyp in human serum samples with satisfactory results.Relying on the rapidly increasing capacity of computing clusters and hardware, convolutional neural networks (CNNs) have been successfully applied in various fields and achieved state-of-the-art results. Despite these exciting developments, the huge memory cost is still involved in training and inferring a large-scale CNN model and makes it hard to be widely used in resource-limited portable devices. To address this problem, we establish a training framework for three-dimensional convolutional neural networks (3DCNNs) named QTTNet that combines tensor train (TT) decomposition and data quantization together for further shrinking the model size and decreasing the memory and time cost. Through this framework, we can fully explore the superiority of TT in reducing the number of trainable parameters and the advantage of quantization in decreasing the bit-width of data, particularly compressing 3DCNN model greatly with little accuracy degradation. In addition, due to the low bit quantization to all parameters during the inference process including TT-cores, activations, and batch normalizations, the proposed method naturally takes advantage in memory and time cost. Experimental results of compressing 3DCNNs for 3D object and video recognition on ModelNet40, UCF11, and UCF50 datasets verify the effectiveness of the proposed method. The best compression ratio we have obtained is up to nearly 180× with competitive performance compared with other state-of-the-art researches. Moreover, the total bytes of our QTTNet models on ModelNet40 and UCF11 datasets can be 1000× lower than some typical practices such as MVCNN.We study the approximation of two-layer compositions f(x)=g(?(x)) via deep networks with ReLU activation, where ? is a geometrically intuitive, dimensionality reducing feature map. We focus on two intuitive and practically relevant choices for ? the projection onto a low-dimensional embedded submanifold and a distance to a collection of low-dimensional sets. We achieve near optimal approximation rates, which depend only on the complexity of the dimensionality reducing map ? rather than the ambient dimension. Since ? encapsulates all nonlinear features that are material to the function f, this suggests that deep nets are faithful to an intrinsic dimension governed by f rather than the complexity of the domain of f. In particular, the prevalent assumption of approximating functions on low-dimensional manifolds can be significantly relaxed using functions of type f(x)=g(?(x)) with ? representing an orthogonal projection onto the same manifold.